Visualization Of Geographic Data Research Articles

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Transmission dynamics, responses, and clinical features for the first 1100 COVID-19 cases in South Batinah, Oman: Major lessons from a provincial perspective

ObjectivesThis study was aimed at exploring and analyzing the epidemiological profile, surveillance, and response to COVID-19, including transmission dynamics and cluster formation. MethodologyThis was a retrospective analysis of surveillance data, including contact tracing, risk factors, and clinical information. Binary logistic regressions were used to assess the likelihood of admission, cluster formation, and of each individual being an index patient. Clusters were demonstrated through geographic data systems, network analysis, and visualization software. ResultsA total of 1100 COVID-19 cases were diagnosed from 20 March to 7 June 2020, of which 144 (13.1%) were asymptomatic. The median time from symptom onset to admission was 7 days (IQR, 4.5–10), and the median symptom duration was 5 days (IQR, 3–9). Eighty-nine clusters containing 736 patients were identified. The surveillance and control actions were divided into three phases. Clusters began to form in phase 2 and became more pronounced in phase 3. Patients ≥50 years of age and patients presenting with fever had relatively higher odds of admission: OR = 12.85 (95% CI 5.13–32.19) and 2.53 (95% CI 1.24–5.17), respectively. Cluster formation was observed among females, asymptomatic patients, and people living in Awabi: OR = 2.3 (95% CI 1.7–3.1), 6.39 (95% CI 2.33–17.2), and 3.54 (95% CI 2.06–6.07), respectively. Patients working in the police and defense sectors had higher odds of being an index patient: OR = 7.88 (95% CI 3.35–18.52). ConclusionCase-based interventions should be supported by population-wide measures, particularly movement restrictions. Establishing prevention teams or district units, or primary care will be crucial for the control of future pandemics. Prevention should always be prioritized for vulnerable populations.

Cancer InFocus: Tools for Cancer Center Catchment Area Geographic Data Collection and Visualization.

The NCI added Community Outreach and Engagement (COE) requirements for NCI-designated cancer centers in 2017, including the charge of characterizing the cancer burden in the geographic area served by their center (i.e., catchment area). Doing so helps cancer centers better identify needs and areas of inequality in their populations to guide research and outreach. To accomplish this, current and comprehensive data must be gathered from multiple sources and analyzed by the COE-a task that is tedious and inefficient. In this paper we present an efficient solution, known as Cancer InFocus, to collecting and visualizing quantitative data that we have generalized for use by other cancer centers on their catchment areas. Cancer InFocus utilizes open source programming languages and modern data collection techniques to gather and transform publicly-available data from various sources for use in specific geographic contexts. Cancer InFocus delivers a choice of two routes for creating interactive online mapping applications that visualize cancer incidence and mortality rates, along with relevant social determinant and risk factor variables, at various geographic levels for a defined cancer center catchment area. Generalized software has been produced to collect and visualize data on any set of U.S. counties, which can be automated to continue providing the most up-to-date data. Cancer InFocus provides tools for cancer centers to accomplish the critical task of maintaining current and comprehensive catchment area data. The open source format will facilitate future enhancements through user collaboration.

WAVES (Web-based tool for Analysis and Visualization of Environmental Samples)-a web application for visualization of wastewater pathogen sequencing results.

Environmental monitoring of pathogens provides an accurate and timely source of information for public health authorities and policymakers. In the last 2 years, wastewater sequencing proved to be an effective way of detection and quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants circulating in population. Wastewater sequencing produces substantial amounts of geographical and genomic data. Proper visualization of spatial and temporal patterns in these data is crucial for the assessment of the epidemiological situation and forecasting. Here, we present a web-based dashboard application for the visualization and analysis of data obtained from sequencing of environmental samples. The dashboard provides multi-layered visualization of geographical and genomic data. It allows to display frequencies of detected pathogen variants as well as individual mutation frequencies. The features of WAVES (Web-based tool for Analysis and Visualization of Environmental Samples) for early tracking and detection of novel variants in the wastewater are demonstrated in an example of BA.1 variant and the signature Spike mutation S: E484A. WAVES dashboard is easily customized through the editable configuration file and can be used for different types of pathogens and environmental samples. WAVES source code is freely available at https://github.com/ptriska/WavesDash under MIT license. A demo version of this application can be accessed at: https://wavesdashboard.azurewebsites.net/.

Spatial data warehouse: an analysis in tourism sector of west java province

In the tourism business, the use of geospatial, sometimes known as geospatial information systems, is essential to the collection of data in various different ways. A collection of historical data and a set of tools to enable decision-making are both components of a geographic data warehouse. In this study, we investigate the requirements for developing a recommendation of spatial data warehouse (SDW) that makes advantage of the implementation the geographical data analysis and data visualization in tourism sector. Methodology of this research using qualitative analysis. The SDW Tourism sector technology model, on which work has been going on for some time, will be a driving factor in this study that aim to create a recommendation for integrated tourism data environments to assist with decision-making. It is possible to bring together in a single location not only non-spatial data but also spatial data, as well as applications that are now running on multiple platforms and databases. The output of this research makes a recommendation to construct a spatial data warehouse based on existing data, and diagram of how the data from the data warehouse will be used in the tourism sector.

User-Centered Design as a Method for Engaging Users in the Development of Geovisualization: A Use Case of Temperature Visualization

Visualization of geographic data is part of many widely used solutions that aim to communicate the information to the end user. Effective visualizations are those that are tailored to a specific group of users and their tasks, as well as to specific properties of the data. Usability is a key concept for such solutions, and the most effective way to achieve high usability is to incorporate user-centered design (UCD) into the development process. In visualization, data are often coded by colors, so the choice of color schemes and color range is critical to enable the reverse mapping of colors to data. In this paper, we present a method for integrating the principles of UCD into the development process. In doing so, we use a case involving the selection of the most appropriate color scheme and associated parameters for representing temperature values on geographic maps. The method described is suitable for use in UCD-based research related to the visualization of various types of data and is useful for researchers and developers of geovisualizations.

Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods.

Identifying SARS-CoV-2 regional introductions and transmission clusters in real time.

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Reactions to geographic data visualization of infectious disease outbreaks: an experiment on the effectiveness of data presentation format and past occurrence information

ObjectivesThis study intended to compare the effectiveness of thematic maps with that of tabular data in comprehension and memory of risk magnitudes, with Zika virus (ZIKV) disease outbreaks in the United States as the subject matter. The study also aimed to examine the effects of data presentation format and past occurrence information on risk perception and risk avoidance intention. Study designThis study used an experiment. MethodsEach participant was randomly assigned to view ZIKV disease 2017 incidence data presented in one of the three formats: a choropleth map, a graduated-circle map, and a table, after which they answered questions about comprehension and memory of risk magnitudes. Each participant was then randomly assigned to view or not to view incidence data of the previous occurrence of ZIKV outbreaks in 2016, after which they answered questions about risk perception and risk avoidance intention. ResultsThe results revealed the effectiveness of thematic maps over tabular data in comprehension, risk perception, and risk avoidance intention. Compared to tabular data, the choropleth map led to a better comprehension of relative risk magnitudes, the graduated-circle map led to higher risk perception, and both thematic maps led to greater risk avoidance intention. In contrast, tabular data led to better recognition of absolute risk magnitudes than both thematic maps. In addition, past occurrence information enhanced risk perception and risk avoidance intention. ConclusionsThe findings reveal the importance of data presentation format in comprehension and memory of risk magnitudes. This can be attributed to the cognitive match between the information emphasized in the presentation and that required by the tasks. The findings also suggest that data presentation format and past occurrence information are important judgmental heuristics that help to form risk perception and risk avoidance intention.

Hierarchical visualization of geographical areal data with spatial attribute association

Geographical areal data usually presents hierarchical structures, and its characteristics vary at different scales. At the higher scales, the visualization of geographical areal data is abstract and the detailed features are easily missed. As a difference, more detailed information is presented at the lower scales while the visual perception of global features is easily disturbed due to the overdrawing of visual elements. As the geographical areal data is visualized at a single scale at the same time, it seems impossible to balance the visual perception of both the global features and detailed characteristics. In this paper, we propose a multi-scale geographical areal data visualization method based on spatial attribute association to enhance the visual perception of both the global features and detailed characteristics. Firstly, the geographical areal data is aggregated into hierarchical clusters based on the spatial similarity. Then, the coefficient of variation is applied to estimate the attribute distribution of each cluster in the hierarchy, and a novel geographical areal data visualization scheme is proposed to adaptively present the multi-scale clusters with lower variation coefficients at the same time. In addition, a rich set of visual interfaces and user-friendly interactions are provided enabling users to specify those clusters of interest at different scales and compare multi-scale visualizations with different hierarchies. Finally, we implement a geographical areal data visualization framework, allowing users to visually explore the global features and detailed characteristics at the same time and get deeper insights into the potential features in the geographical areal data. Case studies and quantitative comparisons based on real-world datasets have been conducted to demonstrate the effectiveness of the proposed multi-scale visualization method for in-depth visual exploration of geographical areal data.

Land Economic Efficiency and Improvement of Environmental Pollution in the Process of Sustainable Urbanization: Case of Eastern China

Economic development, environmental protection and land resources are important components in sustainable cities. According to the environmental Kuznets curve, developing countries are prone to environmental pollution problems while developing their economies. At the same time, as urbanization progresses, the problem of inadequate land resources and land use efficiency in China is coming to the fore. Although China is a developing country, it began to actively implement environmental protection measures years ago in an effort to transform itself into an innovative country. Therefore, as an economic and policy pioneer region, can eastern China benefit from all three aspects of land–economy–environment at the same time? Or will the increase in land economic efficiency (Land_EcoE) and the improvement of environmental pollution occur simultaneously? With the characteristics of land use efficiency and other concepts, this study combines economic factors and land factors to establish a Land_EcoE evaluation system. On the basis of mapping the spatio-temporal evolution of carbon emissions and Land_EcoE, and discussing the spatio-temporal evolution characteristics and correlation between them initially and visually by means of geographic data visualization, this study uses the data of 84 prefecture-level cities and municipalities directly under the central government in eastern China from 2011 to 2017 to test the research hypotheses from a quantitative perspective. Specifically, this study analyzes the correlation between Land_EcoE and environmental pollution by constructing a panel regression model. The conclusions show that, in general, the increase in Land_EcoE in eastern China is associated with the increase in carbon emissions. For a group of prefecture-level cities with the most developed economies in eastern China, the increase in Land_EcoE is correlated with the decrease in carbon emissions. Based on this research, this study proposes a series of policy implications on how to promote simultaneous economic–land–environmental benefits.

3D Tiles-Based High-Efficiency Visualization Method for Complex BIM Models on the Web

Geographic data visualization is an important research area of Web Geographic Information System (GIS). Owing to the detailed subassemblies and exhaustive knowledge database, building information modeling (BIM) plays an important role in geospatial research and industries. The integration of BIM and GIS contributes to the smooth visualization, quick construction, and efficient management of geographic data. However, there are very few methods that can yield high-efficiency data transmission and visualization for complex BIM models while maintaining the integrity of the internal subassembly structure and attributes. To overcome this issue, this paper proposes a 3D Tiles-based visualization method for complex BIM models on the Web-based 3D model viewer. This method is adopted to partition the BIM model according to its assembly without simplifying the BIM model, by using a tiling method for 3D models based on a degraded R-tree, which accounts for the size of tiles. Subsequently, we introduce the “Mask Filter,” a level of detail method that is used to layer the BIM model. Conducting a series of contrast experiments, the result indicates that this method is efficient and feasible, which significantly improves visualization performance of complex BIM with mass data in the geospatial scene and facilitates the integration of Building Information Modeling and Geographic Information System.

Co-occurrence, possible origin, and health-risk assessment of arsenic and fluoride in drinking water sources in Mexico: Geographical data visualization

Arsenic and fluoride in drinking water present a significant challenge to public health worldwide. In this study, we analyze the results of one of the largest surveys of drinking water quality in Mexico: 14,058 samples from 3951 sites, collected between January and December 2017. We use these data to identify the distribution and possible origin of arsenic and fluoride in drinking water throughout the country, and to estimate the associated health burden. The highest concentrations appear in alluvial aquifers in arid northern Mexico, where high-silica volcanic rock likely releases both arsenic and fluoride to the groundwater. We find fluoride contamination to be significantly correlated with aridity (Pearson correlation = −0.45, p = 0.0105), and also find a significant difference in fluoride concentrations between arid and humid states (Welch's t-test, p = 0.004). We estimate population exposure by assigning to each town in Mexico the average concentration of any sampling sites within 5 km. Our results show that 56% of the Mexican population lives within 5 km of a sampling site, 3.05 million people are exposed to fluoride above the reference dosage of 0.06 mg/(kg ∗ day), 8.81 million people are exposed to arsenic above the limit of 10 μg/L, and an additional 13,070 lifetime cases of cancer are expected from this arsenic exposure alone. This burden of disease is concentrated in the arid states of north-central Mexico.

An Online Geographic Data Visualization Tool to Relate Preterm Births to Environmental Factors

Preterm birth (<37 weeks gestation) continues to be a significant cause of disease and death in the United States. Its complex causes are associated with several genetic, biological, environmental, and sociodemographic factors. Organizing and visualizing various data that may be related to preterm birth is an essential step for pattern exploration and hypothesis generation and presents an opportunity to increase public and stakeholder involvement. In this article, we describe a collaborative effort to create an online geographic data visualization tool using open software to explore preterm birth in Fresno County, where rates are the highest in California. The tool incorporates information on births, environmental exposures, sociodemographic characteristics, the built environment, and access to care. We describe data sets used to build the tool, the data-hosting platform, and the process used to engage stakeholders in its creation. We highlight an important example of how collaboration can increase the utility of geographic data visualization to improve public health and address health equity in birth outcomes.

3D Visualization Process and Efficiency Analysis of Geographic Information Based on WebGL

Abstract. With the rapid development of geographic information technology, the web-based 3D visualization technology is constantly updated with the development of computers. However, the traditional 3D geographic data visualization software based on the rich-client model it has significant limitations in the display and analysis of geospatial information and it is difficult to install and use, result in that difficult to meet the application of Geo-dynamic simulation, visual analysis, and collaborative decision-making of the geographic process. In order to show the realism of spatial data more directly, this paper introduces a 3D visualization process design method of geographic information and analyses the 3D data modelling building and visualization efficiency. The process combines multiple data sources are loaded automatically, cross platform, 2D &amp;amp; 3D integration, and make a wider range of customers easily access to the visualization effect of 3D spatial data.

ECatch: Enabling collaborative fisheries management with technology

Modernizing data systems to inform collaborative management is critical to adaptively managing fisheries in an era of climate change. In 2006, The Nature Conservancy of California purchased 13 federal groundfish permits in California with the objective of managing the fishing and reporting activities in a manner that protected sensitive habitats and species. At that time, collecting data for this fishery was done with paper logbooks. This made queries and visualization that could inform management decisions towards our objective impossible in a timely manner. To solve this problem, we built successive software prototypes that leveraged location-aware mobile devices, cloud-based computing, and visualization and query of geographic data over the web. The resulting software, eCatch, enabled avoidance of sensitive species and habitats and quantitative reporting on performance metrics related to those activities. What started as a technology solution to a problem of timely scientific monitoring revealed collateral benefits of collaboration with the fishing industry and markets that support sustainable activities.

Enhancing Geographical Data Visualization through Python: A Comprehensive Study

Geographical records visualization plays a pivotal position in comprehending spatial information, aiding decision-making tactics, and fostering powerful communique in numerous fields. This studies affords a comprehensive examine on improving geographical statistics visualization via the utilization of Python programming language. As the demand for insightful and interactive visualizations continues to grow, Python has emerged as a effective device with a plethora of libraries and frameworks devoted to spatial facts representation. The literature review delves into the present landscape of geographical data visualization, outlining traditional techniques, modern-day equipment, and the expanding function of Python on this area. The study provides an in-intensity exploration of prominent Python libraries, consisting of GeoPandas, Matplotlib, Plotly, and Folium, highlighting their features, strengths, and weaknesses. Through a sequence of compelling case studies, the studies illustrates the actual-international impact of Python-based totally geographical visualizations throughout diverse applications consisting of urban making plans, Environmental studies, and epidemiology. The technique section details the step-by way of-step system of making powerful visualizations, encompassing information preprocessing, exploration, and the choice of suitable Python gear. Challenges inherent in geographical statistics visualization are diagnosed, and the research proposes practical solutions and first-rate practices inside the Python surroundings. Looking in the direction of the future, the look at explores rising tendencies in geographical data visualization and anticipates advancements in Python tools for managing spatial records. The research concludes by means of summarizing key findings, emphasizing the pivotal position of Python in advancing geographical records visualization, and suggesting avenues for destiny research to address evolving demanding situations and opportunities on this dynamic area. This comprehensive examine contributes precious insights for researchers, practitioners, and choice-makers seeking to leverage Python for better geographical statistics visualization.

Using Problem-Based Learning (PBL) to Teach Geographic Information Science

Geographic Information Science (GIScience) is a field of study that investigates the development and use of theories, methods, technology, and data for understanding geographic processes, relationships, and patterns (Mark, 2003). Students in the discipline learn the conceptual and technical implementation of Geographic Information Systems (GIS; the analysis, storage, visualization, and management of geographic data). However, GIScience undergraduates often struggle to relate GIS theory to technical practice. In particular, students have difficulty mastering GIS tools and software and they are not well-equipped to determine the series of processes/tools required to complete geoprocessing tasks without prompts. GIScience courses commonly provide students with detailed step-by-step instructions on how to execute various GIS tools in order to solve example problems but students are eventually expected to perform the same or similar problem-solving tasks without detailed instructions.&#x0D; This workshop focuses on how to teach the technical and problem-solving skills required in GIScience courses effectively by employing a problem-based learning (PBL) model. PBL is an active learning method that increases understanding and competency. The approach focuses on problem solving, self-directed learning, team participation and cooperation (Pawson et al., 2006). PBL encourages students to use critical thinking, engages their curiosity to solve real-world problems, and promotes inquiry and interest in the subject matter (Pawson et al., 2006). A PBL approach encourages students to collaboratively solve problems in GIScience by first identifying the general steps to solve the problem and then solve those problems by determining the tools needed to process the data to come to a solution (Melero, 2010). Incorporating PBL into GIScience courses enables students to solve a larger variety of problems, promotes stronger retention of skills and theory, and better prepares them for future professional opportunities and/or academic research.

Tracking Medical Students and Graduates from Hometown to Practice using Geographic Information Systems, 2011-2017

Background: Indiana University School of Medicine (IUSM) through its statewide system provides medical education and clinical experiences across Indiana for more than 1,450 medical students in nine regions, including Indianapolis and eight regional medical campuses (RMCs). The majority of medical students begin their education distributed among the RMCs, and some students continue clinical experiences at the RMCs for their third and fourth years.&#x0D; Methods: We used a Geographic Information System (GIS) to visualize and document the contribution of our RMCs in Indiana to the physician workforce pipeline. Using data from the 2011-2017 graduating classes, we created a geospatial database that linked key information from student records and public domain sources (e.g., hometown, campus assignment, PGY1 specialty, and practice) with their corresponding locational information. ArcGIS 10.5 GIS software was used to produce a series of maps to visualize patterns in student outcomes among the RMCs over time.&#x0D; Results: By using GIS to track medical trainees from hometown to practice, we can provide accurate visualization of extensive geographic data. The maps revealed similarities and differences among the RMCs not fully appreciated until visually mapped, including the specialty choice of students from different campuses and the proportion of graduates practicing in medically underserved areas of the state.&#x0D; Conclusions: GIS maps and their analyses can identify strengths and distinctions among the RMCs while providing accurate descriptions of the role of geography in the professional development journey of the physician workforce. This ongoing project is a major step toward integrating GIS as a familiar tool in academic administration and health workforce research to assist future decision-making by the school leadership.

Analysis and visualization of seafloor geographical and geological data to construct information infrastructure for marine mineral resources exploration - Case study at the Mid-Okinawa Trough -

Analysis and visualization of seafloor geographical and geological data to construct information infrastructure for marine mineral resources exploration - Case study at the Mid-Okinawa Trough -